A Lifting Wavelet Domain Audio Watermarking Algorithm Based on the Statistical Characteristics of Sub-Band Coefficients

• Autorzy: Tao Z. , Zhao H. , Wu J. , Gu J. , Xu Y. , Wu D.
• Języki publikacji: EN

Abstrakty

EN

In this paper, a new lifting wavelet domain audio watermarking algorithm based on the statistical characteristics of sub-band coefficients is proposed. First of all, an original audio signal was segmented and each segment was divided into two sections. Then, the Barker code was used for synchronization, the LWT (lifting wavelet transform) was performed on each section, a synchronization code and a watermark were embedded into the first section and the second section, respectively, by modifying the statistical average value of the sub-band coefficients. The embed strength was determined adaptively according to the auditory masking property. Experiments show that the embedded watermark has better robustness against common signal processing attacks than present algorithms based on LWT and can resist random cropping in particular.

Słowa kluczowe

EN

audio watermarking; lifting wavelet transform; statistical characteristics; sub-band coefficients

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2010
• Tom: Vol. 35, No. 4
• Strony: 481--491
• Opis fizyczny: Bibliogr. 14 poz., wykr.

Twórcy

autor

Tao Z.

autor

Zhao H.

autor

Wu J.

autor

Gu J.

autor

Xu Y.

autor

Wu D.

• Soochow University School of Electronic Information Suzhou 215006, China,

Bibliografia

• 1. Bai J. et al. (2008), SAR Image Denoising Based on Lifting Directionlet Domain Gaussian Scale Mixtures Model, Chinese Journal of Computers, 31, 7, 1234-1241.
• 2. Daubechies I., Sweldens W. (1998), Factoring wavelet transforms into lifting's steps, Journal of Fourier Analysis and Applications, 4, 3, 245-267.
• 3. Gao S.W. et al. (2007), Lifting wavelet transform and its application in digital watermarking. Application Research of Computers, 24, 6, 201-206.
• 4. Qiang Y., Wang Y. (2004), A Survey of Wavelet-domain Based Digital Image Watermarking Algorithm, Computer Engineering and Applications, 40, 11, 46-50.
• 5. Qu J.Y. et al. (2006), Audio digital watermarking based on the lifting scheme wavelet transform, Computer & Digital Engineering, 34, 4, 91-94.
• 6. Sweldens W. (1997), The lifting scheme: a construction of second generation wavelets, SIAM Journal Mathematical Analysis, 29, 2, 511-546.
• 7. Tao Z. et al. (2006), Audio watermarking based on psychoacoustic model and critical band wavelet transform, Acta Acustica, 31, 2, 114-119.
• 8. Wang X.Y. et al. (2005), Content-based adaptive digital audio watermarking algorithm in wavelet domain, Mini-Micro Systems, 26, 8, 1354-1357.
• 9. Wang X.Y. et al. (2006), A new adaptive digital audio watermarking algorithm, Mini-Micro Systems, 27, 7, 1353-1357.
• 10. Wang R.D., Xu D.W. (2006), Multiple audio watermarks based on lifting wavelet transform, Journal of Electronics &Information Technology, 28, 10, 1820-1826.
• 11. Wei L. (2004), Research on Robust Digital Audio Watermarking, PhD thesis, Fudan University, Shanghai.
• 12. Wim S. (1996), The Lifting Scheme: A Custom-Design Construction of Biorthogonal Wavelets. Applied and Computational Harmonic Analysis, 3, 15, 186-200.
• 13. Xiang S.J. (2006),Robust Audio Watermarking Algorithms, PhD thesis, Sun Yat-sen University, Guangzhou.
• 14. Xu D.W., Wang R.D. (2006), A blind audio watermarking algorithm based on convolutional codes, Computer Applications, 26, 7, 1649-1651.